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2086 The value of emergency care data set (ECDS) presentation codes for predicting mortality and inpatient admission
  1. Betsy Teresa,
  2. Adrian Boyle,
  3. Mohammed Subhi
  1. Cambridge University Hospital NHS Foundation Trust


Aims and Objectives Early identification of patients at higher risk of death and hospital admission is an important problem in emergency departments. Most triage scales were developed before current electronic healthcare records were developed.

The implementation of a national Emergency Care Data Set allows for standardised recording of presenting complaints and use of electronic patient records (EPR) offers the potential for automated triage. The mortality risk and need for hospital admission associated with the different presenting complaints in a standardised national data set has not been previously reported. This study aimed to quantify the risks of death and hospitalisation from presenting complaints. This would be valuable in developing automated triage tools and decision support software.

Method and Design We conducted an observational retrospective cohort study on patients who visited ED in 2021.

Data regarding the initial presentation of a patient was obtained from a single hospital’s EPR. The presenting complaints related to subsequent attendances were excluded. This patient list was then manually matched with a routinely collected list of deaths. All deaths that occurred within 30 days of attendance were included.

Results and Conclusion Data was collected from 84,999 patients, of which 1,159 people died within 30 days of attendance.

The mortality rate was the highest in Cardiac arrest (78.05%), Cardiac arrest due to Trauma (50%) and Respiratory arrest (50%). Drowsy (11.97%), Hypothermia (13.04%) and Cyanosis (10%) were also high risk categories. Chest pain was not a high risk presenting complaint. (table 1)

The initial presenting complaint in ECDS shows a useful ability to identify people at high and lower risk of death. This information may be useful for building automated triage models.

Abstract 2086 Table 1

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